In order to raise the quality of mobility, driver assistance systems (DAS) offer a means to enhance, among other things, active and integrated safety. Nowadays, the building of advanced driver assistance systems (ADAS) to support rather than replace human drivers has become a trend in current intelligent vehicle research. These systems support drivers by strengthening their sensing ability, warning in case of error, and reducing the controlling efforts of drivers.
An ADAS system usually uses more than one kind of sensor: image sensors, lidar, and radar, etc. No single sensor can provide input as complete, robust, and accurate. Image sensors have some problems, such as low ability of sensing depth and higher ability of discrimination than lidar and radar. Radar shows limited lateral spatial information because it is not available at all, the field of view is narrow, or the resolution is reduced at large distances. Although lidar has a wide view field that solves part of the previous problems, there are other problems such as low ability of discrimination, clustering error, and recognition latency. Ultrasonic sensors are used to detect obstacles in the surrounding environment, but they are usually complemented by rearview cameras to better assist the driver with more detailed information. These restrictions of the different sensor types explain the attention given to sensor fusion in research on object detection and tracking, as well as to the fuller exploitation of each sensor type.
The disclosed method and system for vision-centric deep-learning-based road situation analysis are directed to solve one or more problems set forth above and other problems.